Hasil untuk "Computer applications to medicine. Medical informatics"

Minmin Wang, Xinran Lu, Yeting Du et al.

Abstract In this study, we review the application of digital technologies for health in China, examining the structure of its digital health governance. China’s digital health governance is of political commitment, cross-sectoral collaboration, and a comprehensive, all-encompassing approach that engages the entire society. However, the fragmentation of data remains a fundamental obstacle. The whole-of-society approach offers a valuable example for other low- and middle-income countries in promoting digital transformation.

Jianbo Qing, Jialu Wu, Yafeng Li et al.

Abstract Background The emergence of single-cell (SC) and spatial transcriptomics (ST) has revolutionized our understanding of gene expression dynamics in complex tissues. However, it also presents challenges for data analysis and visualization, particularly due to the complexity of ST data and the diversity of analysis platforms. The SCNT (Single-Cell, Single-Nucleus, and Spatial Transcriptomics Analysis and Visualization Tools) package was developed to address these challenges by providing an efficient and user-friendly tool for processing, analyzing, and visualizing SC and ST data. Results SCNT is an R-based package that integrates widely used tools such as Seurat and ggplot2, enabling seamless conversion between Seurat and H5ad formats. The package supports high-resolution spatial visualization, including customizable gene expression and clustering plots. SCNT also simplifies key data analysis steps, such as quality control, dimensionality reduction, and doublet detection, significantly enhancing workflow efficiency. We tested SCNT on publicly available PBMC dataset, Visum and Visium HD human kidney tissue data, demonstrating its effectiveness. Conclusions SCNT offers a valuable tool for researchers exploring SC and ST data. Its simplicity, flexibility, and powerful visualization capabilities provide a streamlined workflow for both novice and advanced users. Future developments will focus on expanding support for additional ST platforms and enhancing multi-omics data integration.

Tessa Oakley, Juliao Vaz, Fausto da Silva et al.

Abstract Background Effective diagnostic capacity is crucial for clinical decision-making, with up to 70% of decisions in high-resource settings based on laboratory test results. However, in low- and middle-income countries (LMIC) access to diagnostic services is often limited due to the absence of Laboratory Information Management Systems (LIMS). LIMS streamline laboratory operations by automating sample handling, analysis, and reporting, leading to improved quality and faster results. Despite these benefits, sustainably implementing LIMS in LMIC is challenging due to high costs, inadequate infrastructure, and limited technical expertise. Methods This study evaluated the implementation of a customised microbiology LIMS at the National Health Laboratory (NHL) in Timor-Leste. The LIMS was deployed in November 2020, with an accompanying online results portal introduced in early 2021. The implementation was assessed via a checklist based on key challenges and requirements for LIMS in LMIC, alongside a post-implementation survey of scientists and clinicians. Results The assessment revealed significant improvements in laboratory processes, including enhanced sample throughput, data management, and result reporting. The LIMS reduced transcription errors and standardised reporting of antimicrobial susceptibility testing (AST), improving data quality and accessibility. However, challenges such as unreliable internet connectivity and the need for ongoing funding and technical support persist. The user satisfaction survey, with responses from 19 laboratory scientists and 15 clinicians, revealed positive feedback on workflow improvements and result accessibility, although concerns about internet speed, sustainability, and the need for further training were noted. Conclusion This study highlights the importance of careful planning, customisation, and stakeholder engagement in LIMS implementation in LMIC. The success in Timor-Leste demonstrates the potential for improved laboratory quality and patient outcomes, but also underscores the need for ongoing investment in infrastructure, technical expertise, and sustainability planning.

Emily Jade Young, Heather Mary Kirk

BackgroundHeavy menstrual bleeding (HMB) is a common condition that affects approximately 20% to 30% of women globally. However, despite significant physical, mental, and social impacts on the quality of life of women who experience HMB, they face barriers to both diagnosis and treatment. With current challenges to female reproductive autonomy growing on a global scale and with the stigma that surrounds menstruation, women with HMB may turn to online communities to access peer support and information. Online forums such as Reddit, which support the use of anonymous posting, may offer a space where those affected by HMB can share their experiences, seek support, and offer advice to others. ObjectiveThis study aimed to explore how those experiencing HMB use Reddit to share experiential knowledge, provide support, and share experiences of HMB within an online community space. MethodsData were collected from discussion threads on the TwoXChromosomes subreddit on Reddit. Publicly accessible posts were identified through a systematic search conducted on August 13 and 14, 2024, using keywords related to HMB. A template approach to thematic analysis was used to analyze the data. A priori codes were developed from existing literature on HMB and the research objective. The template was refined after further examination of the transcripts, with all transcripts being analyzed using the final template. ResultsThe search initially identified 434 discussion threads. Threads were screened for relevance and user engagement, resulting in a final 13 (2.99%) threads being analyzed for this research. These comprised 1505 individual comments from 1115 unique users. Four central themes were identified: validation and camaraderie, life impacts of HMB, practical support, and medical treatment and management. In the validation and camaraderie theme, users frequently shared personal experiences and validated the experiences of others, challenging the normalization of debilitating symptoms and creating a shared sense of solidarity. When discussing the life impacts of HMB, users emphasized how it disrupts daily functioning, including work, relationships, and mental well-being, and poses serious physical health risks. In the theme of practical support, Reddit users exchanged strategies for managing symptoms, including recommending specific menstrual products, home and workplace adaptations and adjustments, and self-advocacy. The final theme of medical treatment and management explored Reddit users’ frustration with health care experiences, particularly around the prioritization of fertility over quality of life. Hormonal contraception, intrauterine devices, and surgical interventions were discussed with varying degrees of satisfaction and concern. Overall, Reddit users reported a general dismissal of HMB within medical and social contexts. ConclusionsReddit serves as an important platform for individuals with HMB to validate their experiences, share practical knowledge, and seek peer support in the face of medical dismissal. This research provides insight into the usefulness of online spaces for people discussing HMB.

Thomas Savage, Stephen P Ma, Abdessalem Boukil et al.

Abstract BackgroundLarge language model (LLM) fine-tuning is the process of adjusting out-of-the-box model weights using a dataset of interest. Fine-tuning can be a powerful technique to improve model performance in fields like medicine, where LLMs may have poor out-of-the-box performance. The 2 common fine-tuning techniques are supervised fine-tuning (SFT) and direct preference optimization (DPO); however, little guidance is available for when to apply either method within clinical medicine or health care operations. ObjectiveThis study aims to investigate the benefits of fine-tuning with SFT and DPO across a range of core natural language tasks in medicine to better inform clinical informaticists when either technique should be deployed. MethodsWe use Llama3 8B (Meta) and Mistral 7B v2 (Mistral AI) to compare the performance of SFT alone and DPO across 4 common natural language tasks in medicine. The tasks we evaluate include text classification, clinical reasoning, text summarization, and clinical triage. ResultsOur results found clinical reasoning accuracy increased from 7% to 22% with base Llama3 and Mistral2, respectively, to 28% and 33% with SFT, and then 36% and 40% with DPO (PPPF1PF1PPF1PP ConclusionsSFT alone is sufficient for simple tasks such as rule-based text classification, while DPO after SFT improves performance on the more complex tasks of triage, clinical reasoning, and summarization. We postulate that SFT alone is sufficient for simple tasks because SFT strengthens simple word-association reasoning, whereas DPO enables deeper comprehension because it is trained with both positive and negative examples, enabling the model to recognize more complex patterns. Ultimately, our results help inform clinical informaticists when to deploy either fine-tuning method and encourage commercial LLM providers to offer DPO fine-tuning for commonly used proprietary LLMs in medicine.

Yuyan Jiang, Xue-li Liu, Liyun Wang

BackgroundSome scholars who are skeptical about open-access mega journals (OAMJs) have argued that low-quality papers are often difficult to publish in more prestigious and authoritative journals, and OAMJs may be their main destination. ObjectiveThis study aims to evaluate the academic quality of OAMJs and highlight their important role in clinical medicine. To achieve this aim, authoritative journals and representative OAMJs in this field were selected as research objects. The differences between the two were compared and analyzed in terms of their level of disruptive innovation. Additionally, this paper explored the countries and research directions for which OAMJs serve as publication channels for disruptive innovations. MethodsIn this study, the journal information, literature data, and open citation relationship data were sourced from Journal Citation Reports (JCR), Web of Science (WoS), InCites, and the OpenCitations Index of PubMed Open PMID-to-PMID citations (POCI). Then, we calculated the disruptive innovation level of the focus paper based on the local POCI database. ResultsThe mean Journal Disruption Index (JDI) values for the selected authoritative journals and OAMJs were 0.5866 (SD 0.26933) and 0.0255 (SD 0.01689), respectively, showing a significant difference. Only 1.48% (861/58,181) of the OAMJ papers reached the median level of disruptive innovation of authoritative journal papers (MDAJ). However, the absolute number was roughly equal to that of authoritative journals. OAMJs surpassed authoritative journals in publishing innovative papers in 24 research directions (eg, Allergy), accounting for 40.68% of all research directions in clinical medicine. Among research topics with at least 10 authoritative papers, OAMJs matched or exceeded MDAJ in 35.71% of cases. The number of papers published in authoritative journals and the average level of disruptive innovation in each country showed a linear relationship after logarithmic treatment, with a correlation coefficient of –0.891 (P<.001). However, the number of papers published in OAMJs in each country and the average level of disruptive innovation did not show a linear relationship after logarithmic treatment. ConclusionsWhile the average disruptive innovation level of papers published by OAMJs is significantly lower than that of authoritative journals, OAMJs have become an important publication channel for innovative research in various research directions. They also provide fairer opportunities for the publication of innovative results from limited-income countries. Therefore, the academic community should recognize the contribution and value of OAMJs to advancing scientific research.

Yunmeng ZHANG, Shenglin LIU, Qingmin FENG et al.

Naked-eye 3D display technology has excellent 3D visual effects and does not require wearable devices assistance. It can present the depth, position and complex structure information of 3D medical images, allowing viewers to obtain information about tissues and organs from different points, reducing cognitive load, contributing to medical teaching and opening up innovative methods for planning and diagnosis. Naked-eye 3D augmented reality display can display medical images in real 3D space, achieving virtual and real vision. It helps a lot to medical research. The applications of naked-eye 3D display technology in three major aspects of medical diagnosis, clinical surgery and rehabilitation training is reviewed in the study. It provides the direction for the subsequent research in medical field, thus assisting medical research and improving medical practice.

Ahmed Saihood, Wijdan Rashid Abdulhussien, Laith Alzubaid et al.

Abstract Background The detection and classification of lung nodules are crucial in medical imaging, as they significantly impact patient outcomes related to lung cancer diagnosis and treatment. However, existing models often suffer from mode collapse and poor generalizability, as they fail to capture the complete diversity of the data distribution. This study addresses these challenges by proposing a novel generative adversarial network (GAN) architecture tailored for semi-supervised lung nodule classification. Methods The proposed DDDG-GAN model consists of dual generators and discriminators. Each generator specializes in benign or malignant nodules, generating diverse, high-fidelity synthetic images for each class. This dual-generator setup prevents mode collapse. The dual-discriminator framework enhances the model’s generalization capability, ensuring better performance on unseen data. Feature fusion techniques are incorporated to refine the model’s discriminatory power between benign and malignant nodules. The model is evaluated in two scenarios: (1) training and testing on the LIDC-IDRI dataset and (2) training on LIDC-IDRI, testing on the unseen LUNA16 dataset and the unseen LUNGx dataset. Results In Scenario 1, the DDDG-GAN achieved an accuracy of 92.56%, a precision of 90.12%, a recall of 95.87%, and an F1 score of 92.77%. In Scenario 2, the model demonstrated robust performance with an accuracy of 72.6%, a precision of 72.3%, a recall of 73.82%, and an F1 score of 73.39% when testing using Luna16 and an accuracy of 71.23%, a precision of 67.56%, a recall of 73.52%, and an F1 score of 70.42% when testing using LungX. The results indicate that the proposed model outperforms state-of-the-art semi-supervised learning approaches. Conclusions The DDDG-GAN model mitigates mode collapse and improves generalizability in lung nodule classification. It demonstrates superior performance on both the LIDC-IDRI and the unseen LUNA16 and LungX datasets, offering significant potential for improving diagnostic accuracy in clinical practice.

Samuel Carbunaru, Yassamin Neshatvar, Hyungrok Do et al.

BackgroundPrediction models based on machine learning (ML) methods are being increasingly developed and adopted in health care. However, these models may be prone to bias and considered unfair if they demonstrate variable performance in population subgroups. An unfair model is of particular concern in bladder cancer, where disparities have been identified in sex and racial subgroups. ObjectiveThis study aims (1) to develop a ML model to predict survival after radical cystectomy for bladder cancer and evaluate for potential model bias in sex and racial subgroups; and (2) to compare algorithm unfairness mitigation techniques to improve model fairness. MethodsWe trained and compared various ML classification algorithms to predict 5-year survival after radical cystectomy using the National Cancer Database. The primary model performance metric was the F1-score. The primary metric for model fairness was the equalized odds ratio (eOR). We compared 3 algorithm unfairness mitigation techniques to improve eOR. ResultsWe identified 16,481 patients; 23.1% (n=3800) were female, and 91.5% (n=15,080) were “White,” 5% (n=832) were “Black,” 2.3% (n=373) were “Hispanic,” and 1.2% (n=196) were “Asian.” The 5-year mortality rate was 75% (n=12,290). The best naive model was extreme gradient boosting (XGBoost), which had an F1-score of 0.860 and eOR of 0.619. All unfairness mitigation techniques increased the eOR, with correlation remover showing the highest increase and resulting in a final eOR of 0.750. This mitigated model had F1-scores of 0.86, 0.904, and 0.824 in the full, Black male, and Asian female test sets, respectively. ConclusionsThe ML model predicting survival after radical cystectomy exhibited bias across sex and racial subgroups. By using algorithm unfairness mitigation techniques, we improved algorithmic fairness as measured by the eOR. Our study highlights the role of not only evaluating for model bias but also actively mitigating such disparities to ensure equitable health care delivery. We also deployed the first web-based fair ML model for predicting survival after radical cystectomy.

Ha Na Cho, Tae Joon Jun, Young-Hak Kim et al.

BackgroundTransformer-based language models have shown great potential to revolutionize health care by advancing clinical decision support, patient interaction, and disease prediction. However, despite their rapid development, the implementation of transformer-based language models in health care settings remains limited. This is partly due to the lack of a comprehensive review, which hinders a systematic understanding of their applications and limitations. Without clear guidelines and consolidated information, both researchers and physicians face difficulties in using these models effectively, resulting in inefficient research efforts and slow integration into clinical workflows. ObjectiveThis scoping review addresses this gap by examining studies on medical transformer-based language models and categorizing them into 6 tasks: dialogue generation, question answering, summarization, text classification, sentiment analysis, and named entity recognition. MethodsWe conducted a scoping review following the Cochrane scoping review protocol. A comprehensive literature search was performed across databases, including Google Scholar and PubMed, covering publications from January 2017 to September 2024. Studies involving transformer-derived models in medical tasks were included. Data were categorized into 6 key tasks. ResultsOur key findings revealed both advancements and critical challenges in applying transformer-based models to health care tasks. For example, models like MedPIR involving dialogue generation show promise but face privacy and ethical concerns, while question-answering models like BioBERT improve accuracy but struggle with the complexity of medical terminology. The BioBERTSum summarization model aids clinicians by condensing medical texts but needs better handling of long sequences. ConclusionsThis review attempted to provide a consolidated understanding of the role of transformer-based language models in health care and to guide future research directions. By addressing current challenges and exploring the potential for real-world applications, we envision significant improvements in health care informatics. Addressing the identified challenges and implementing proposed solutions can enable transformer-based language models to significantly improve health care delivery and patient outcomes. Our review provides valuable insights for future research and practical applications, setting the stage for transformative advancements in medical informatics.

Noha Maddah, Arpana Verma, Maryam Almashmoum et al.

BackgroundMass gatherings (MGs; eg, religious, sporting, musical, sociocultural, and other occasions that draw large crowds) pose public health challenges and concerns related to global health. A leading global concern regarding MGs is the possible importation and exportation of infectious diseases as they spread from the attendees to the general population, resulting in epidemic outbreaks. Governments and health authorities use technological interventions to support public health surveillance and prevent and control infectious diseases. ObjectiveThis study aims to review the evidence on the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MG events. MethodsA systematic literature search was conducted in January 2022 using the Ovid MEDLINE, Embase, CINAHL, and Scopus databases to examine relevant articles published in English up to January 2022. Interventional studies describing or evaluating the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MGs were included in the analysis. Owing to the lack of appraisal tools for interventional studies describing and evaluating public health digital surveillance systems at MGs, a critical appraisal tool was developed and used to assess the quality of the included studies. ResultsIn total, 8 articles were included in the review, and 3 types of MGs were identified: religious (the Hajj and Prayagraj Kumbh), sporting (the Olympic and Paralympic Games, the Federation International Football Association World Cup, and the Micronesian Games), and cultural (the Festival of Pacific Arts) events. In total, 88% (7/8) of the studies described surveillance systems implemented at MG events, and 12% (1/8) of the studies described and evaluated an enhanced surveillance system that was implemented for an event. In total, 4 studies reported the implementation of a surveillance system: 2 (50%) described the enhancement of the system that was implemented for an event, 1 (25%) reported a pilot implementation of a surveillance system, and 1 (25%) reported an evaluation of an enhanced system. The types of systems investigated were 2 syndromic, 1 participatory, 1 syndromic and event-based, 1 indicator- and event-based, and 1 event-based surveillance system. In total, 62% (5/8) of the studies reported timeliness as an outcome generated after implementing or enhancing the system without measuring its effectiveness. Only 12% (1/8) of the studies followed the Centers for Disease Control and Prevention guidelines for evaluating public health surveillance systems and the outcomes of enhanced systems based on the systems’ attributes to measure their effectiveness. ConclusionsOn the basis of the review of the literature and the analysis of the included studies, there is limited evidence of the effectiveness of public health digital surveillance systems for infectious disease prevention and control at MGs because of the absence of evaluation studies.

M Julkarnain, Mohammad Taufan Asri Zaen, Nawassyarif Nawassyarif et al.

This study aims to design and build learning applications for medicinal plants based on Augmented reality so that they can be used as interactive learning media. The research methods used are qualitative and quantitative methods. Data collection methods used in this study are observation, interview, documentation, questionnaires and literature study and use the Prototype method as a software development method. This Augmented Reality-based medicinal plant learning application was built using the C# and Blender programming language to create 3-dimensional image designs. The result of developing this application is a learning media that can be run on a Smartphone by utilizing interactive Augmented Reality technology. When this application is opened, the main page will appear containing several menus, namely the play, guide, learning, and quite menus. The features contained in Augmented Reality-Based Medicinal Plant Learning, namely the 3D object scan feature, can find out information about plants with scanned images. This application can help people to get to know medicinal plants and their properties. And it can be an effective learning medium. Based on the results of the questionnaire, the application made by the author received a positive response from the respondents, in many category, such as the display, educational, satisfaction, interactive and music

Su Yang, Jose Miguel Sanchez Bornot, Ricardo Bruña Fernandez et al.

Abstract Magnetoencephalography (MEG) has been combined with machine learning techniques, to recognize the Alzheimer’s disease (AD), one of the most common forms of dementia. However, most of the previous studies are limited to binary classification and do not fully utilize the two available MEG modalities (extracted using magnetometer and gradiometer sensors). AD consists of several stages of progression, this study addresses this limitation by using both magnetometer and gradiometer data to discriminate between participants with AD, AD-related mild cognitive impairment (MCI), and healthy control (HC) participants in the form of a three-class classification problem. A series of wavelet-based biomarkers are developed and evaluated, which concurrently leverage the spatial, frequency and time domain characteristics of the signal. A bimodal recognition system based on an improved score-level fusion approach is proposed to reinforce interpretation of the brain activity captured by magnetometers and gradiometers. In this preliminary study, it was found that the markers derived from gradiometer tend to outperform the magnetometer-based markers. Interestingly, out of the total 10 regions of interest, left-frontal lobe demonstrates about 8% higher mean recognition rate than the second-best performing region (left temporal lobe) for AD/MCI/HC classification. Among the four types of markers proposed in this work, the spatial marker developed using wavelet coefficients provided the best recognition performance for the three-way classification. Overall, the proposed approach provides promising results for the potential of AD/MCI/HC three-way classification utilizing the bimodal MEG data.

Teresa Neuparth, André M. Machado, Rosa Montes et al.

The use of transcriptomics data brings new insights and works as a powerful tool to explore the molecular mode of action (MoA) of transgenerational inheritance effects of contaminants of emerging concern. Therefore, in this dataset, we present the transcriptomic data of the transgenerational effects of environmentally relevant simvastatin levels, one of the most prescribed human pharmaceuticals, in the keystone amphipod species Gammarus locusta. In summary, G. locusta juveniles were maintained under simvastatin exposure up to adulthood (exposed group - F0E) and the offspring of F0E were transferred to control water for the three subsequent generations (transgenerational group - F1T, F2T and F3T).To gain insights into the biological functions and canonical pathways transgenerationally disrupted by simvastatin, a G. locusta de novo transcriptome assembly was produced and the transcriptomic profiles of three individual G. locusta females, per group, over the four generations (F0 to F3) - solvent control groups (F0.C, F1.C, F2.C and F3.C), F0 320 ng/L simvastatin exposed group (F0.320E) and F1 to F3 320 transgenerational group (F1.320T; F2.320T and F3.320T) - were analyzed. Briefly, Illumina HiSeq™ 2500 platform was used to perform RNA sequencing, and due to the unavailability of G. locusta genome, the RNA-seq datasets were assembled de novo using Trinity and annotated with Trinotate software. After assembly and post-processing steps, 106093 transcripts with N50 of 2371 bp and mean sequence length of 1343.98 bp was produced. BUSCO analyses showed a transcriptome with gene completeness of 97.5 % Arthropoda library profile. The Bowtie2, RSEM and edgeR tools were used for the differential gene expression (DEGs) analyses that allowed the identification of a high quantity of genes differentially expressed in all generations. Finally, to identify the main metabolic pathways affected by the transgenerational effects of SIM across all generations, the DGEs genes were blasted onto KEGG pathways database using the KAAS webserver. The data furnished in this article allows a better molecular understanding of the transgenerational effects produced by simvastatin in the keystone amphipod G. locusta and has major implications for hazard and risk assessment of pharmaceuticals and other emerging contaminants. This article is related to the research article entitled “Transgenerational inheritance of chemical-induced signature: a case study with simvastatin [1].

Reiko Onodera, Shintaro Sengoku

Jun Guo, Hu Wen, Yin Liu et al.

Data in this article presents the characteristic parameters of spontaneous combustion of coal with different ranks, including lignite, long flame coal, and anthracite. The coal samples were tested by the temperature programmed method. The gas concentration data produced at different temperature points during the heating process are obtained. Through monitoring the spontaneous combustion of coal in a coal mine, the field data in goaf are obtained. Through processing on the data from the experiment and field, three gas indices were obtained, which include CO/CO2, Graham value and Alkane ratio. The data is made available for further use and for furthering the understanding of the key findings of the related research, such as the early warning for spontaneous combustion of coal. For more insight please see A method for evaluating the spontaneous combustion of coal by monitoring various gases (Guo et al., 2019). Keywords: Coal spontaneous combustion, Temperature-programmed experiments, Various index gases, Environmental pollution control

Yit-Lai Chow, Fumihiko Sato

This article presents small RNA sequencing data of Caenorhabditis elegans consist of P0 control worms (untreated), P0 worms treated with a plant alkaloid, sanguinarine, and its F3 offspring. The data were analyzed to identify microRNAs that were differentially expressed in both the sanguinarine-treated P0 and its descendants F3 worms. Targets of the identified miRNAs, gene function annotations and their functional clusters are shown. The data presented here will facilitate comparison with data from other researchers who are working on miRNAs profiling of xenobiotic-treated C. elegans. Keywords: Caenorhabditis elegans, Sanguinarine, MicroRNA, Benzylisoquinoline alkaloids, Transgenerational inheritance

Huibin Jia, Yanwei Li, Dongchuan Yu

Although autism spectrum disorder (ASD) was previously found to be associated with aberrant brain structure, neuronal amplitudes and spatial neuronal interactions, surprisingly little is known about the temporal dynamics of neuronal oscillations in this disease. Here, the hemoglobin concentration signals (i.e., oxy-Hb and deoxy-Hb) of young children with ASD and typically developing (TD) children were recorded via functional near infrared spectroscopy (fNIRS) when they were watching a cartoon. The long-range temporal correlations (LRTCs) of hemoglobin concentration signals were quantified using detrended fluctuation analysis (DFA). Compared with TD group, the DFA exponents of young children with ASD were significantly smaller over left temporal region for oxy-Hb signal, and over bilateral temporo-occipital regions for deoxy-Hb signals, indicating a shift-to-randomness of brain oscillations in the children with ASD. Testing the relationship between age and DFA exponents revealed that this association could be modulated by autism. The correlation coefficients between age and DFA exponents were significantly more positive in TD group, compared to those in ASD group over several brain regions. Furthermore, the DFA exponents of oxy-Hb in left temporal region were negatively correlated with autistic symptom severity. These results suggest that the decreased DFA exponent of hemoglobin concentration signals may be one of the pathologic changes in ASD, and studying the temporal structure of brain activity via fNIRS technique may provide physiological indicators for autism. Keywords: Long-range temporal correlations, Autism spectrum disorder, fNIRS, Left temporal region

Amos Cahan, Sorel Cahan, James J. Cimino

Matthew D Krasowski, Scott R Davis, Denny Drees et al.

Background: Autoverification is a process of using computer-based rules to verify clinical laboratory test results without manual intervention. To date, there is little published data on the use of autoverification over the course of years in a clinical laboratory. We describe the evolution and application of autoverification in an academic medical center clinical chemistry core laboratory. Subjects and Methods: At the institution of the study, autoverification developed from rudimentary rules in the laboratory information system (LIS) to extensive and sophisticated rules mostly in middleware software. Rules incorporated decisions based on instrument error flags, interference indices, analytical measurement ranges (AMRs), delta checks, dilution protocols, results suggestive of compromised or contaminated specimens, and ′absurd′ (physiologically improbable) values. Results: The autoverification rate for tests performed in the core clinical chemistry laboratory has increased over the course of 13 years from 40% to the current overall rate of 99.5%. A high percentage of critical values now autoverify. The highest rates of autoverification occurred with the most frequently ordered tests such as the basic metabolic panel (sodium, potassium, chloride, carbon dioxide, creatinine, blood urea nitrogen, calcium, glucose; 99.6%), albumin (99.8%), and alanine aminotransferase (99.7%). The lowest rates of autoverification occurred with some therapeutic drug levels (gentamicin, lithium, and methotrexate) and with serum free light chains (kappa/lambda), mostly due to need for offline dilution and manual filing of results. Rules also caught very rare occurrences such as plasma albumin exceeding total protein (usually indicative of an error such as short sample or bubble that evaded detection) and marked discrepancy between total bilirubin and the spectrophotometric icteric index (usually due to interference of the bilirubin assay by immunoglobulin (Ig) M monoclonal gammopathy). Conclusions: Our results suggest that a high rate of autoverification is possible with modern clinical chemistry analyzers. The ability to autoverify a high percentage of results increases productivity and allows clinical laboratory staff to focus attention on the small number of specimens and results that require manual review and investigation.